Paper short abstract:

This paper proposes a qualitative methodology for the analysis of digital tracking data. By exploring the qualitative dimensions of digital tracking, this paper contributes to advancing methodologies for studying evolving media consumption behaviors in the digital age.

Paper long abstract:

This paper introduces an innovative qualitative approach to tackle the challenges of digitization through a study of consumption habits in the media sector. Traditional methods, based on quantitative surveys and qualitative techniques such as observation and interviews, find themselves challenged in the evolving digital landscape. The swift movement of users within a digital environment with abundant choices, coupled with issues of survey recall and the intricacies of online intermediary structures, complicates the assessment of usage patterns (Grigoropoulou and Small 2022; Parry et al 2021; Thorson and Wells 2016).

The emergence of digital tracking offers a new avenue for investigation (Stier et al 2020). Participants use apps or browser plug-ins, enabling researchers to explore connections between information access, political preferences, and sociodemographic characteristics (Jürgens and Stark 2022; Merten et al 2022; Scharkow et al 2020). However, analyzing tracking data in the social science context poses significant challenges due to the enormous volume of data.

This paper proposes a qualitative methodology for the analysis of extensive tracking data, inspired by sequence analysis (Abbott 1995; Borgna and Struffolino 2018). With a representative sample of German internet users, the paper aims to uncover various patterns of incidental and habitual information acquisition by linking usage paths on mobile phones and desktop computers with collected online news content and attitudes expressed in an open survey item on the Russian invasion of Ukraine. The paper critically discusses the limitations of this technique and suggests approaches for a more robust mixed-methods analysis of extensive digital trace data.